As technology continues to change our lives in many profound ways, artificial intelligence (AI) has emerged as the most profound and transformative development, seemingly capable of transforming entire industries.
Business operations and how we work within our organizations may also change depending on how organizational leaders leverage AI and adopt best practices to harness its full potential.
Artificial Intelligence – the Basics
Artificial intelligence leverages several different technologies to simulate human intelligence to perform tasks that would normally be carried out by people, such as making decisions, recognizing speech, and recognizing and describing images.
AI Technologies
AI technologies can automate tasks and enhance existing functionalities. The following are a few examples:
- Machine learning (ML) can teach computers to learn from data without being specifically programmed. By training on data models, ML can recognize patterns and predict outcomes, find relationships, make decisions, and receive feedback.
- Natural language processing (NLP) uses computational linguistics to interpret and interact with human language, performing tasks such as translation, speech and text recognition. A well-known use of NLP is spam detection.
- Computer vision teaches machines to interpret and draw information from pictures and videos. Learning models teach machines to improve human visuals and are currently used in signature recognition and analyzing medical images.
- Robotic process automation (RPA) automates tasks or actions by deploying scripts that emulate human actions and processes. This technology is used to perform repetitive tasks in the office and the broader enterprise and is used in many industries.
Using AI to Achieve Business Goals
There are three main use cases for leveraging AI in your organization, all of which are meant to meet and exceed business goals:
Improve Efficiency and Customer Service
AI can allow your organization to automate repetitive tasks and run certain procedures with fewer errors. Some services can even be automated to run 24/7, such as using chatbots to help place orders or provide assistance. Amazon uses AI for multiple use cases, such as customer recommendations, enhancing shopping experiences and improving logistics to ensure faster delivery.
Provide Competitive Advantages
AI-powered solutions can offer innovative ways to deliver more personalized options for customers, set your companies’ products and services apart from the competition, and ensure that users stay engaged. Netflix has continuously been a leader in streaming media due to its use of AI. The company uses ML algorithms to analyze viewing habits and match users to their preferences.
Improve Decision-Making
AI’s powerful data analysis capabilities can allow users of AI tools to make more accurate decisions based on real-time insights. This not only applies to business leaders but also to many employees working in healthcare, finance, marketing and other industries who rely on the most up-to-date insights to perform their jobs.
Artificial Intelligence Process
Building an AI system requires several key elements. The first and most important step is to identify the problem you are trying to solve and define clear goals for the system. Once this is determined, six steps take you through system development:
- Data collection: A critical step is collecting the data that will train your new AI system. Data can be text, images, audio and video, and is generally structured or unstructured.
- Data cleansing: Once the data is collected, it needs to be ready to train the AI models that you will be building. To do this, review the data for several aspects:
- Make sure the data is complete.
- Correct any incorrect data.
- Ensure that the data is current and relevant.
- Model selection: In this step, you will choose a model or algorithm best suited to solving the current problem. Several types of models are available, including machine learning models, deep learning models and hybrid models.
- Model training: Once the data has been cleansed, split it into two sets: a training set and a validation set. As the training data is fed into the algorithm, ensure consistency in quality. If there are large data sets, consider using a tool to manage the process.
- Testing and optimizing: Using the validation data (which contains the expected output), review the initial output for accuracy and make note of any problems. If you encounter problems, the reason could be inaccurate data, the model may not have captured patterns, or the data may be biased. As you continue to test, this will allow you to modify and optimize the parameters of the model.
- Deployment: Once testing is completed, it is time to integrate the model into your existing systems or deploy a new system along with the new model.
Artificial Intelligence Best Practices
Understanding how AI can help your organization to flourish is not enough. Implementing it strategically, understanding ethical considerations, ensuring that your data is of the highest quality, and addressing security and privacy concerns must be top of mind. A recent Gartner survey found that 52% of companies that have already deployed AI in their organizations consider risk factors when considering new use cases.
With that in mind, below are several artificial intelligence procedures to consider:
Include AI strategy Into Organizational Goals
Clearly define in the business objectives what implementing AI hopes to achieve. Identify the areas where AI can deliver the most value, whether it is task automation, improving customer service or yielding insights from data.
Align your AI goals with your strategic goals by including specific language that sets clear objectives.
The way you use AI may change fairly quickly. For example, you may decide to start using AI to help answer customer questions faster. In doing so, you might realize that AI can help develop new product features and ideas. Having AI goals as strategic goals will allow your organization to show value while providing the flexibility to pivot.
Develop a Strong Ethics Framework
Address ethical concerns associated with AI, such as bias and discrimination, by developing an ethics framework. Consider including the following aspects:
- Bias and fairness
- Transparency and accountability
- Data privacy and security
- Human oversight
As the framework is being developed, talk with as many people in your company as possible to ensure their engagement. Everyone must understand how the company is using AI and how to use it properly.
Ensure High-quality Data
Data governance must be the foundation on which any AI initiative is based. If the data is not complete and correct, the implications could be incorrect answers and misleading choices.
Start by knowing where your data originates and how it is collected. Have a process for regularly checking data to make sure it is accurate and current. This means removing errors and identifying missing information.
Address All Security and Privacy Concerns
Realizing the potential of any AI initiatives you set in motion will require safeguards. Technology has always been an accelerator and an enabler, with potential risks that need to be managed. First, all organizational leaders must be committed and involved. These leaders must help build the governance structures and determine which actions to take if a risk develops.
Be sure to include privacy and regulatory compliance guardrails to build trust with customers, employees, regulators and other stakeholders. Ensure what you are building meets regulatory requirements such as the General Data Protection Regulation (GDPR) and HIPAA.
Finally, ensure that all engineering is research-driven and that all technology leaders will put responsible AI practices into any solution.
Artificial Intelligence Tools
Thinking of leveraging a tool that has already been developed? Many can help your business right now.
Generative AI Tools
Generative AI tools made the headlines in late 2022, when OpenAI publicly released ChatGPT in November 2022.
Generative AI tools create text, images, audio and video based on user-submitted natural language prompts. They serve a broad set of industries and can be used by both small and large organizations, from lawyers to sales representatives to marketers.
These tools can work on their own or can often be embedded into websites and applications.
Customer Service AI Tools
These AI tools are a natural fit for customer service because they can be quickly trained and deployed. Another plus is that they are always available. Generally speaking, these AI tools can learn your customer service processes and guidelines and use chatbots to assist customers by providing a route to the answers.
Human Resources AI Tools
Current AI HR tools can automate certain HR services and assist with recruiting. For example, some will assist with onboarding and answering employee questions, and some can even measure employee experiences. Regarding recruiting assistance, these tools can generate and send personalized emails to potential candidates, as well as tell you which candidates opened their emails.
Artificial Intelligence Templates
Every organization that implements artificial intelligence needs a set of guidelines and regulations to ensure that all tools and data are used responsibly. Consider creating the following:
- A corporate artificial intelligence policy
- An artificial intelligence governance framework
- A plan to monitor AI performance, with regular reviews
- A plan to ensure that all AI tools adhere to legal and regulatory standards
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